Review of Classical Statistical Estimation 1. Parameters of probability distribution e.g. , , , are constants (unknown) 2. Observed data Choice of probability distribution Point estimates of parameter Computation of probability 3. Statistical uncertainty of estimation described in terms of confidence intervals not incorporated in probability computation (same applies to model error) 4. Subjective judgmental information not incorporated in estimation 5. Large error may incur if sample size is small Accident Rate at an Intersection Theoretical Model Previous Experiences Computer Simulation Observed performance first week 1 per year 1 in =? mean rate of accident EXAMPLE – SHORT PILE p = fraction of short piles 1. Engineer’s Experience & Judgement P’(p = pi) .4 .3 .15 0 .2 .4 .6 .1 .05 .8 1.0 pi p̂ = mean of p = 0.44 2. After 1 successful detection test, i.e. pile selected for inspection is found to be short P”(p = 0.2) = P(p = 0.2 | successful test) P(suc.test | p = 0.2)P(p = 0.2) = P(suc.test | p = 0.2)P(p = 0.2)+...+P(suc.test | p = 1.0)P(p = 1.0) 0.2 0.3 = 0.2 0.3 0.4 0.4 0.6 0.15 0.8 0.1 1.0 0.05 = 0.136 P”(p = pi) .364 p̂ = 0.55 .204 .182 .136 0 .2 .4 .6 .8 .114 1.0 pi P”(p = pi) P”(p = pi) P”(p = pi) 0.5 0.5 0.5 n=2 0.2 0.4 0.6 n=6 n=4 0.8 pi 1.0 P”(p = pi) 0.2 0.4 0.6 0.8 1.0 P”(p = pi) pi 0.2 n = 10 0.5 0.6 0.8 pi 1.0 Fig. E8.1c 0.2 0.4 0.6 0.8 1.0 pi 0.2 0.4 Case of no specific prior information diffused prior distribution .2 .2 .2 Prior PMF .2 Prior PDF p 0 1.0 pi 0.5 0.6 PMF of p for increasing number of successful tests .2 0.8 n= 0.5 0.4 0.6 P”(p = pi) n=8 0.2 0.4 1 0.8 1.0 pi Incorporation of Statistical Uncertainty into Probability Calculation Problem: Suppose three piles are selected at random to be inspected in a new but similar site, what is the probability that none of these 3 piles will be short? (say, after 1 succ. test) 1. Classical method: No judgement use point estimate p̂ = 1.0 3 0 3 P(X 0) 1 0 0 0 2. Semi-Bayesian: Include judgment use point estimate p̂ = 0.55 3 P(X = 0) = 0 (0.55)0(0.45)3 = 0.09 3. Bayesian Method: Include Judgement, use entire distribution of p (through theorem of total probability) P(X = 0) = P(X=0 | p=0.2)P”(p=0.2) +…+P(X=0 | p=1)P”(p=1) = (0.8)3 0.136 + (0.6)3 0.364 + (0.4)3 0.204 + (0.2)3 0.182 = 0.163 In short, Bayesian approach 1) allows judgmental information to be incorporated 2) permits updating of probability and distribution of parameter 3) combines the statistical uncertainty of parameter with basic variability of the random variable Let be the parameter Posterior Prior P”( = i) P( | i ) P '( i ) P ( i | ) P( | i ) P '( i ) i observed data P( X a) P( X a | i ) P "( i ) i Bayesian probability Continuous Case Prior f’() Posterior f"() i f "( i ) P”( = i) i + i P( | i ) f '( i ) P( | i ) f '( i ) i P( | ) f '( ) as 0, f "( ) P( | ) f '( )d In short Normalizing constant such that f "( )d 1.0 f"() = k L() f’() Likelihood function P( | ) i.e. given value what is probability of observing data k L( ) f ' ( )d P( X a) 1 P( X a | ) f "( )d